Collapsing ordered outcome categories: a note of concern.

Abstract:

:When analyzing and interpreting data from an epidemiologic study where ordinal (ordered categorical) outcomes have been measured in different exposure groups, an effect parameter of interest is the common odds ratio implied by the proportional odds model. This model can sometimes be applied to a collapsed outcome variable, instead of the measured variable, without reducing efficiency considerably. However, in a given data set, changing the outcome categories can affect the effect estimate as well as the inference being drawn from the data, even if the true effect itself has not changed. In particular, one should be careful in dichotomizing the measured outcome variable.

journal_name

Am J Epidemiol

authors

Strömberg U

doi

10.1093/oxfordjournals.aje.a008944

subject

Has Abstract

pub_date

1996-08-15 00:00:00

pages

421-4

issue

4

eissn

0002-9262

issn

1476-6256

journal_volume

144

pub_type

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